Libraries:
library(tidyverse)
Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
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library(readr)
library(rvest)
Loading required package: xml2
Attaching package: 㤼㸱rvest㤼㸲
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library(stats)
library(readxl)
library(dplyr)
library(stringr)
library(ggplot2)
library(ggthemes)
package 㤼㸱ggthemes㤼㸲 was built under R version 3.6.2
library(stringr)
library(data.table)
Registered S3 method overwritten by 'data.table':
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print.data.table
data.table 1.12.6 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com
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library(geojsonio)
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library(leaflet)
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library(rgdal)
Loading required package: sp
rgdal: version: 1.4-7, (SVN revision 845)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
Path to GDAL shared files: C:/Users/neris/OneDrive/Documents/R/win-library/3.6/rgdal/gdal
GDAL binary built with GEOS: TRUE
Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
Path to PROJ.4 shared files: C:/Users/neris/OneDrive/Documents/R/win-library/3.6/rgdal/proj
Linking to sp version: 1.3-1
library(haven)
library(stargazer)
Please cite as:
Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
Data:
Model 3:
First making a graph of annual GDP highs and lows – maybe a temporary proxy for recessions?
Annual enrollment graph:
Join enrollment data and gdp data to create linear model test:
Graph it?
College Proximity Question 5/3: (Reading in Ivy’s data)
cz <- read_dta('cz.dta')
Error in read_dta("cz.dta") : could not find function "read_dta"
Read in/create mobility data: (Trends in Mobility: Commuting Zone Intergenerational Mobility Estimates by Birth Cohort) https://opportunityinsights.org/data/?geographic_level=101&topic=0&paper_id=0#resource-listing
Read in geojson file:
Commuting zones on the map (cz.geojson@data) are in 1990s format. They need to be converted so our post-2000 data can be connected to the shapefiles: (https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/)
Making the map actually display data:
cz.geo %>%
leaflet() %>%
addTiles() %>%
addPolygons(fillColor = ~colors1(cz.geo@data$ncollege),
weight = 1,
color = "white",
opacity = 0.5,
fillOpacity = .7) %>%
setView(-96, 37.8, 3) %>%
addLegend(pal = colors1,
values = cz.geo@data$ncollege,
title = "Number of Colleges in Commuting Zone")
Some values were outside the color scale and will be treated as NA
Try to run some lms:
Ivy’s STATA code:
foreach x of varlist ncollege nfouryr nfouryrpriv npub nelite hascollege{
foreach y of varlist kfr_pooled_pooled_p1 kfr_pooled_pooled_p25 kfr_pooled_pooled_p50 kfr_pooled_pooled_p75 kfr_pooled_pooled_p100 {
reg `y' `x' popdensity2010 med_hhinc2016, r
outreg2 using `x'_kfr, excel append ctitle(`y')
}
Variables of interest:
as.formula(paste0(yvar1, " ~ ", paste0(xvars1, collapse = " + ")))
kfr_pooled_pooled_p1 ~ ncollege + nfouryr + nfouryrpriv + npub +
nelite + hascollege + popdensity2010 + med_hhinc2016
testing?
stargazer(lm.kfr_p1,
type = "text",
dep.var.labels = c("kfr_pooled_pooled_p1"))
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
===============================================
Dependent variable:
---------------------------
kfr
-----------------------------------------------
ncollege 0.003
(0.004)
nfouryr 0.007*
(0.004)
nfouryrpriv -0.012***
(0.004)
npub -0.011***
(0.004)
nelite 0.006
(0.005)
hascollege -0.049***
(0.006)
popdensity2010 -0.0001***
(0.00001)
med_hhinc2016 0.00000***
(0.00000)
Constant 0.251***
(0.012)
-----------------------------------------------
Observations 741
R2 0.281
Adjusted R2 0.273
Residual Std. Error 0.062 (df = 732)
F Statistic 35.756*** (df = 8; 732)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
nelite on kfr at different levels
htmtable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
type = "html",
dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
out = "nelitetable.html")
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
<table style="text-align:center"><tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="5"><em>Dependent variable:</em></td></tr>
<tr><td></td><td colspan="5" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left"></td><td>Bottom 1%</td><td>25%</td><td>50%</td><td>75%</td><td>Top 1%</td></tr>
<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">nelite</td><td>-0.004</td><td>-0.002</td><td>-0.001</td><td>0.001</td><td>0.004</td></tr>
<tr><td style="text-align:left"></td><td>(0.003)</td><td>(0.003)</td><td>(0.002)</td><td>(0.002)</td><td>(0.003)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">popdensity2010</td><td>-0.0001<sup>***</sup></td><td>-0.0001<sup>***</sup></td><td>-0.00004<sup>***</sup></td><td>-0.00002<sup>***</sup></td><td>-0.00000</td></tr>
<tr><td style="text-align:left"></td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td><td>(0.00001)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">med_hhinc2016</td><td>0.00000<sup>***</sup></td><td>0.00000<sup>***</sup></td><td>0.00000<sup>***</sup></td><td>0.00000</td><td>-0.00000<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td><td>(0.00000)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td style="text-align:left">Constant</td><td>0.221<sup>***</sup></td><td>0.350<sup>***</sup></td><td>0.471<sup>***</sup></td><td>0.585<sup>***</sup></td><td>0.770<sup>***</sup></td></tr>
<tr><td style="text-align:left"></td><td>(0.012)</td><td>(0.010)</td><td>(0.009)</td><td>(0.009)</td><td>(0.009)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>741</td><td>741</td><td>741</td><td>741</td><td>741</td></tr>
<tr><td style="text-align:left">R<sup>2</sup></td><td>0.141</td><td>0.104</td><td>0.055</td><td>0.016</td><td>0.030</td></tr>
<tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.137</td><td>0.100</td><td>0.051</td><td>0.012</td><td>0.026</td></tr>
<tr><td style="text-align:left">Residual Std. Error (df = 737)</td><td>0.068</td><td>0.057</td><td>0.050</td><td>0.047</td><td>0.053</td></tr>
<tr><td style="text-align:left">F Statistic (df = 3; 737)</td><td>40.261<sup>***</sup></td><td>28.387<sup>***</sup></td><td>14.380<sup>***</sup></td><td>4.096<sup>***</sup></td><td>7.651<sup>***</sup></td></tr>
<tr><td colspan="6" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="5" style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
</table>
image print cheeeeck:
tables %>%
ggplot() +
geom_bar(stat = "identity") +
geom_image(aes(image = picture),
size = .2)
Error: geom_bar requires the following missing aesthetics: x, y

Plotting the lms:

Wait lemme try the long format thing:
write_csv(data_long1a, "long_kfr.csv")
Error in is.data.frame(x) : object 'data_long1a' not found
Output table:
stargazer(long.lm1,
type = "text")
length of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changedlength of NULL cannot be changed
===============================================
Dependent variable:
---------------------------
kfr
-----------------------------------------------
ncollege -0.001***
(0.0003)
black -0.099***
(0.004)
white 0.023***
(0.003)
popdensity2010 -0.00001**
(0.00001)
med_hhinc2016 0.00000***
(0.00000)
Constant 0.478***
(0.007)
-----------------------------------------------
Observations 10,250
R2 0.119
Adjusted R2 0.119
Residual Std. Error 0.142 (df = 10244)
F Statistic 276.960*** (df = 5; 10244)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
Try with interaction & w/o hhinc:
summary(long.lm1a)
Call:
lm(formula = kfr ~ ncollege + black + white + black * ncollege +
white * ncollege + popdensity2010 + med_hhinc2016, data = long.data1a)
Residuals:
Min 1Q Median 3Q Max
-0.39850 -0.10502 -0.01114 0.09142 0.85434
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.787e-01 7.168e-03 66.775 < 2e-16 ***
ncollege -1.284e-03 4.290e-04 -2.993 0.00277 **
blackTRUE -1.006e-01 3.960e-03 -25.394 < 2e-16 ***
whiteTRUE 2.189e-02 3.629e-03 6.031 1.69e-09 ***
popdensity2010 -1.494e-05 7.062e-06 -2.116 0.03440 *
med_hhinc2016 9.841e-07 1.429e-07 6.888 5.99e-12 ***
ncollege:blackTRUE 4.269e-04 5.166e-04 0.826 0.40862
ncollege:whiteTRUE 4.141e-04 5.091e-04 0.814 0.41594
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.142 on 10242 degrees of freedom
(865 observations deleted due to missingness)
Multiple R-squared: 0.1192, Adjusted R-squared: 0.1186
F-statistic: 197.9 on 7 and 10242 DF, p-value: < 2.2e-16
No significant difference. Will stick to original model.
Visualize it maybe?:

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
---
title: "econ401 final project R Notebook"
output: html_notebook
---

Libraries:
```{r libraries, results = "markup"}
library(tidyverse)
library(readr)
library(rvest)
library(stats)
library(readxl)
library(dplyr)
library(stringr)
library(ggplot2)
library(ggthemes)
library(stringr)
library(data.table)
library(geojsonio)
library(leaflet)
library(rgdal)
library(haven)
library(stargazer)
library(RColorBrewer)
```

Data:
```{r data, include = FALSE}
# GDP up to Feb 2020
# https://ihsmarkit.com/products/us-monthly-gdp-index.html
gdp.index.data <- readxl::read_xlsx('US-Monthly-GDP-History-Data.xlsx', sheet = 3)
gdp.index <- gdp.index.data
colnames(gdp.index)[1] <- "Y_M"
year.month <- str_split_fixed(gdp.index$Y_M, ' - ', 2)
colnames(year.month) <- c('Year', 'Month')
gdp.index <- cbind(year.month, gdp.index[, -1])
gdp.annual <- gdp.index %>%
  group_by(Year) %>%
  summarize(MaxGDP = max(`Monthly Real GDP Index`),
            MinGDP = min(`Monthly Real GDP Index`))

# https://nces.ed.gov/programs/digest/d18/tables/dt18_306.10.asp
enrollment.data <- read_xls('tabn306.10.xls')
enrollment <- enrollment.data[1:12]
# enrollment is in thousands
enrollment <- enrollment[-c(1, 3, 15, 27, 39, 51, 63, 75, 99, 111, 123, 135:139), ]
col1 <- data.frame(str_remove_all(enrollment[[1]], '\\.'), stringsAsFactors = FALSE)
col1[2, 1] <- "All_Students"
enrollment <- cbind(col1, enrollment[, -1])
enrollment <- t(enrollment)
rownames(enrollment) <- c()
colnames(enrollment) <- enrollment[1, ]
enrollment <- data.frame(enrollment)
colnames(enrollment)[1] <- 'Year'
enrollment <- enrollment[-1, ]
Years <- as.numeric(str_extract(enrollment$Year, "[:digit:]{4}"))
enrollment <- cbind(Years, enrollment[, -1])
enrollment <- data.frame(lapply(enrollment, function(x){ 
  gsub("---", NA, x)
}))
str(enrollment)

enrollment1 <- enrollment[, 1:2]
gdp.annual1 <- gdp.annual
all.students <- as.numeric(enrollment[4:11, 2])

gdp.annual$Year <- as.factor(gdp.annual$Year)
enrollment1$Years <- as.factor(enrollment1$Years)
enrollment1$All_Students <- as.numeric(as.character(enrollment1$All_Students))
```

Model 3:

  First making a graph of annual GDP highs and lows -- maybe a temporary proxy for recessions?
```{r graph1, include = FALSE}
gdp.annual %>%
  ggplot() +
  geom_line(mapping = aes(x = Year,
                 y = MaxGDP,
                 group = 1)) +
  geom_line(mapping = aes(x = Year,
                          y = MinGDP,
                          group = 1)) +
  theme_economist() +
  ylab('Real GDP')
```

  Annual enrollment graph:
```{r graph2, include = FALSE}
enrollment1 %>%
  ggplot() +
  geom_line(mapping = aes(x = Years,
                          y = All_Students,
                          group = 1)) +
  theme_economist() +
  ylab('Enrollment')
```

  Join enrollment data and gdp data to create linear model test:
```{r lm1, include = FALSE}
test <- inner_join(enrollment1, gdp.annual1,
          by = c("Years" = "Year"))

lm1 <- lm(All_Students ~ MaxGDP,
          data = test,
          na.action = na.omit)
summary(lm1)
```

  Graph it?
```{r graph3_4, include = FALSE}
test %>%
  ggplot() +
  geom_line(aes(x = Years,
                 y = All_Students,
                group = 1)) +
  theme_economist() +
  ylab('Enrollment by All Students')

test %>%
  ggplot() +
  geom_line(aes(x = Years,
                 y = MaxGDP,
                 group = 1)) +
  # geom_abline(slope = 0.8015, intercept = 6316.7207) +
  theme_economist()
```

College Proximity Question 5/3:
(Reading in Ivy's data)
```{r read proximity data, results = "markup"}
# cz_college <- read_dta("cz_college.dta")
cz <- read_dta('cz.dta')
# colleges <- read_dta('colleges.dta')
# mobility.results <- read_xlsx('mobility_results.xlsx')
```

Read in/create mobility data:
(Trends in Mobility: Commuting Zone Intergenerational Mobility Estimates by Birth Cohort)
https://opportunityinsights.org/data/?geographic_level=101&topic=0&paper_id=0#resource-listing
```{r mobility data, include=FALSE}
# mobility.data <- read_xls('onlinedata1_trends.xls')
# colnames(mobility.data) <- mobility.data[15, ]
# mobility <- mobility.data[-c(1:16), ]
# mobility.1986 <- mobility %>%
#   filter(`Birth Cohort` == 1986)
# mobility.1986$`Commuting Zone` <- as.numeric(mobility.1986$`Commuting Zone`)
# cz.mobility.data <- full_join(mobility.1986[, c(1, 3:8)],
#                   cz,
#                   by = c(`Commuting Zone` = 'cz'))
# 
# cz.mobility <- cz.mobility.data[, c(1:8, 2132:2137)]
# cz.mobility <- cz.mobility[, c(1, 8, 9:14, 3:7, 2)]
# write_csv(cz.mobility, 'cz.mobility.csv')
cz.mobility <- read_csv('cz.mobility.csv')
```

Read in geojson file:
```{r geojson}
cz.geojson <- geojson_read("cz1990.json",
                        what = "sp")
# View(cz.geojson@data)
# cz.geojson %>%
#   leaflet() %>%
#   #addTiles() %>%
#   addPolygons() %>%
#   setView(-96, 37.8, 3)
```

Commuting zones on the map (cz.geojson@data) are in 1990s format. They need to be converted so our post-2000 data can be connected to the shapefiles:
(https://www.ers.usda.gov/data-products/commuting-zones-and-labor-market-areas/)
```{r cz shape combine, include = FALSE}
# cz.conversions <- read_xls('cz00_eqv_v1.xls')
# cz.conversions <- cz.conversions[, c(2:4)]
# cz.conversions$`Commuting Zone ID, 1990` <- as.numeric(cz.conversions$`Commuting Zone ID, 1990`)
# cz.conversions$`Commuting Zone ID, 1980` <- as.numeric(cz.conversions$`Commuting Zone ID, 1980`)
# colnames(cz.conversions)[2] <- 'cz1990'
# colnames(cz.conversions)[1] <- 'cz2000'
# colnames(cz.conversions)[3] <- 'cz1980'
# 
# head(cz.geojson@data)
# cz.geo <- cz.geojson
# colnames(cz.mobility)[1] <- 'cz1990'
# 
# cz.geo@data <- full_join(cz.geo@data,
#                   cz.conversions[, -3],
#                   by = c('cz' = 'cz1990'))
# cz.geo@data <- left_join(cz.geo@data,
#                          cz.mobility,
#                          by = c('cz' = 'cz1990'))
# 
# cz.geo %>%
#   leaflet() %>%
#   addPolygons() %>%
#   setView(-96, 37.8, 3)

# geojson_write(cz.geo,
#               file = "cz_geo.geojson",
#               overwrite = TRUE)

cz.geo <- geojson_read("cz_geo.geojson",
                        what = "sp")
```

Making the map actually display data:
```{r}
bins1 <- c(1, 5, 10, 20, 40, 60, 90)
        
colors1 <- colorBin(bins = bins1,
                    palette = "YlOrRd",
                    domain = cz.geo@data$ncollege)
        
cz.geo %>%
  leaflet() %>%
  addTiles() %>%
  addPolygons(fillColor = ~colors1(cz.geo@data$ncollege),
              weight = 1,
              color = "white",
              opacity = 0.5,
              fillOpacity = .7) %>%
  setView(-96, 37.8, 3) %>%
  addLegend(pal = colors1,
            values = cz.geo@data$ncollege,
            title = "Number of Colleges in Commuting Zone")


```


Try to run some lms:

  Ivy's STATA code:

    foreach x of varlist ncollege nfouryr nfouryrpriv npub nelite hascollege{
    	
    foreach y of varlist kfr_pooled_pooled_p1 kfr_pooled_pooled_p25 kfr_pooled_pooled_p50 kfr_pooled_pooled_p75 kfr_pooled_pooled_p100 {
    	reg `y' `x' popdensity2010 med_hhinc2016, r 
    	outreg2 using `x'_kfr, excel append ctitle(`y')
    }
    
  Variables of interest:
  
```{r}
yvar1 <- "kfr_pooled_pooled_p1"
xvars1 <- c("ncollege", "nfouryr", "nfouryrpriv", "npub", "nelite", "hascollege", "popdensity2010", "med_hhinc2016")
cz1 <- cz[, c(yvar1, xvars1)]

lm.model1 <- as.formula(paste0(yvar1, " ~ ", paste0(xvars1, collapse =  " + ")))

```

testing?
```{r}
lm.kfr_p1 <- lm(lm.model1,
                data = cz)
summary(lm.kfr_p1)

stargazer(cz1, type = "text", title="Descriptive statistics", digits=1, out="table1.txt")

stargazer(lm.kfr_p1,
          type = "text",
          dep.var.labels = c("kfr_pooled_pooled_p1"))
 #          ,
 #          covariate.labels = c("Gross horsepower", "Rear axle ratio","Four foward gears",
 # "Five forward gears","Type of transmission (manual=1)"), out="models.txt")


```

nelite on kfr at different levels
```{r}
yvar.p1 <- "kfr_pooled_pooled_p1"
yvar.p25 <- "kfr_pooled_pooled_p25"
yvar.p50 <- "kfr_pooled_pooled_p50"
yvar.p75 <- "kfr_pooled_pooled_p75"
yvar.p100 <- "kfr_pooled_pooled_p100"
xvars.nelite <- c("nelite", "popdensity2010", "med_hhinc2016")
lm.nelite.p1 <- as.formula(paste0(yvar.p1, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p25 <- as.formula(paste0(yvar.p25, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p50 <- as.formula(paste0(yvar.p50, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p75 <- as.formula(paste0(yvar.p75, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
lm.nelite.p100 <- as.formula(paste0(yvar.p100, " ~ ", paste0(xvars.nelite, collapse =  " + ")))
nelite.p1 <- lm(lm.nelite.p1, cz)
nelite.p25 <- lm(lm.nelite.p25, cz)
nelite.p50 <- lm(lm.nelite.p50, cz)
nelite.p75 <- lm(lm.nelite.p75, cz)
nelite.p100 <- lm(lm.nelite.p100, cz)
txttable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
          type = "text",
          title = "The Effect of Elite Colleges in Commuting Zone on the Probability that a Child from the 20th Percentile Falls in Each Income Percentile as an Adult",
          dep.var.caption = "Parent Income Percentile",
          dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
          # notes = "Where nelite is the number of elite colleges in commuting zone (cz), popdensity2010 is the cz's popultion density, and med_hhinc2016 is the median household income in cz in 2016.",
          # notes.append = TRUE,
          # notes.align = "l",
          out = "nelitetable.txt")
htmtable.nelite <- stargazer(nelite.p1, nelite.p25, nelite.p50, nelite.p75, nelite.p100,
          type = "html",
          dep.var.labels = c("Bottom 1%", "25%", "50%", "75%", "Top 1%"),
          out = "nelitetable.html")
summary(lm.nelite.p1)
```
image print cheeeeck:
```{r}
tables <- data.frame(tablename = c("hascollege_black",
                                "hascollege_kfr",
                                "hascollege_top20",
                                "hascollege_white",
                                "ncollege_black",
                                "ncollege_kfr",
                                "ncollege_top20",
                                "ncollege_white",
                                "nelite_black",
                                "nelite_kfr",
                                "nelite_top20",
                                "nelite_white",
                                "nfouryr_black",
                                "nfouryr_kfr",
                                "nfouryr_top20",
                                "nfouryr_white",
                                "nfouryrpriv_black",
                                "nfouryrpriv_kfr",
                                "nfouryrpriv_top20",
                                "nfouryrpriv_white",
                                "npub_black",
                                "npub_kfr",
                                "npub_top20",
                                "npub_white",
                                "sumstats"),
                     picture = c("C:\\\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\hascollege_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\hascollege_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\hascollege_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\hascollege_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\ncollege_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\ncollege_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\ncollege_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\ncollege_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nelite_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nelite_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nelite_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nelite_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryr_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryr_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryr_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryr_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryrpriv_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryrpriv_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryrpriv_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\nfouryrpriv_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\npub_black.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\npub_kfr.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\npub_top20.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\npub_white.png",
                                 "C:\\Users\\neris\\OneDrive - Middlebury College\\Last Semester\\ECON Inequality & Justice\\econ0401\\sumstats.png"))
tables$tablename <- as.character(tables$tablename)
tables$picture <- as.character(tables$picture)

# write_csv(tables, "outputtables.csv")

# tables %>%
#   ggplot() +
#   geom_bar(stat = "identity") +
#   geom_image(aes(image = picture),
#              size = .2)
```


Plotting the lms:

```{r}
lm1 <- lm(kfr_pooled_pooled_mean ~ ncollege + popdensity2010 + med_hhinc2016,
   data = cz)
ncollege.seq <- seq(from = 0, to = 82, by = 5)
kfr_pooled_pooled_mean.seq <- seq(from = 0.2490857, to = 0.6728090, by = .025)
popdensity2010.seq <- seq(from = 0.106, to = 5635.804, by = 300)
med_hhinc2016.seq <- seq(from = 26645, to = 103043, by = 3750)
grid1 <- expand.grid('ncollege' = ncollege.seq,
                     'kfr_pooled_pooled_mean' = kfr_pooled_pooled_mean.seq,
                     'popdensity2010'  = popdensity2010.seq,
                     'med_hhinc2016' = med_hhinc2016.seq)
predictions <- predict.lm(lm1,
                          grid1)
final.data <- data.frame(grid1, predictions)

final.data %>%
  ggplot(aes(x = ncollege,
             y = predictions)) +
  geom_line() +
  stat_smooth(aes(x = ncollege, y = predictions), method = "lm",
              formula = y ~ kfr_pooled_pooled_mean ~ ncollege + popdensity2010 + med_hhinc2016, se = FALSE) +
  theme_economist() +
  scale_color_brewer(palette = "YlOrRd")

ggplot(aes(x = plotx, y = ploty)) %>%
  geom_abline()

lm2 <- lm(kfr_black_pooled_p50 ~ ncollege + popdensity2010 + med_hhinc2016,
   data = cz)
lm3 <- lm(kfr_white_pooled_p50 ~ ncollege + popdensity2010 + med_hhinc2016,
   data = cz)

cz %>%
  ggplot(aes(x = ncollege, y = kfr_pooled_pooled_mean)) +
  geom_abline(intercept = 3.799e-01, slope = -8.572e-04) +
  geom_abline(intercept = 3.523e-01, slope = -9.573e-04) +
  geom_abline(intercept = 4.973e-01, slope = -9.133e-04) +
  ylim(3.5e-01, 0.5)

plot(lm1)

```



Wait lemme try the long format thing:

```{r}
kfr.cols <- c("kfr_black_pooled_p1", "kfr_black_pooled_p25", "kfr_black_pooled_p50", "kfr_black_pooled_p75", "kfr_black_pooled_p100", "kfr_pooled_pooled_p1", "kfr_pooled_pooled_p25", "kfr_pooled_pooled_p50", "kfr_pooled_pooled_p75", "kfr_pooled_pooled_p100", "kfr_white_pooled_p1", "kfr_white_pooled_p25", "kfr_white_pooled_p50", "kfr_white_pooled_p75", "kfr_white_pooled_p100")

kfr.test <- cz[, c(kfr.cols, "ncollege", "nfouryr", "nelite", "npub", "popdensity2010", "med_hhinc2016")]

keycol1 <- "category"
valuecol1 <- "kfr"
gathercols1 <- kfr.cols

data_long <- gather_(kfr.test, keycol1, valuecol1, gathercols1)

data_longa <- data_long %>%
  filter(str_detect(data_long$category, "black", negate = FALSE)) %>%
  mutate(race = "black")

data_longa$category <- data_longa$category %>%
  str_replace_all("kfr_black_pooled_p1", "1") %>%
  str_replace_all("kfr_black_pooled_p25", "25") %>%
  str_replace_all("kfr_black_pooled_p50", "50") %>%
  str_replace_all("kfr_black_pooled_p75", "75") %>%
  str_replace_all("kfr_black_pooled_p100", "100")

data_longb <- data_long %>%
  filter(str_detect(data_long$category, "white", negate = FALSE)) %>%
  mutate(race = "white")

data_longb$category <- data_longb$category %>%
  str_replace_all("kfr_white_pooled_p1", "1") %>%
  str_replace_all("kfr_white_pooled_p25", "25") %>%
  str_replace_all("kfr_white_pooled_p50", "50") %>%
  str_replace_all("kfr_white_pooled_p75", "75") %>%
  str_replace_all("kfr_white_pooled_p100", "100")

data_longc <- data_long %>%
  filter(str_detect(data_long$category, "pooled_pooled", negate = FALSE)) %>%
  mutate(race = "pooled")

data_longc$category <- data_longc$category %>%
  str_replace_all("kfr_pooled_pooled_p1", "1") %>%
  str_replace_all("kfr_pooled_pooled_p25", "25") %>%
  str_replace_all("kfr_pooled_pooled_p50", "50") %>%
  str_replace_all("kfr_pooled_pooled_p75", "75") %>%
  str_replace_all("kfr_pooled_pooled_p100", "100")

long.data1 <- rbind(data_longa, data_longb,
                    data_longc) 

long.data1a <- long.data1 %>%
  mutate(black = ifelse(race == "black",
                        TRUE,
                        FALSE),
         white = ifelse(race == "white",
                        TRUE,
                        FALSE))

long.lm1 <- lm(kfr ~ ncollege + black + white + popdensity2010 + med_hhinc2016,
   data = long.data1a)
summary(long.lm1)

# write_csv(long.data1a, "long_kfr.csv")
# long.data1a <- read_csv('long_kfr.csv')

```

Output table:
```{r}
stargazer(long.lm1,
          type = "text")
```

Try with interaction & w/o hhinc:
```{r}
long.lm1.wohhinc <- lm(kfr ~ ncollege + black + white + popdensity2010,
   data = long.data1a)
long.lm1a <- lm(kfr ~ ncollege + black + white + black*ncollege + white*ncollege + popdensity2010 + med_hhinc2016,
   data = long.data1a)
summary(long.lm1a)
```
No significant difference. Will stick to original model.

Visualize it maybe?:
```{r}
long.lm1.race <- lm(kfr ~ ncollege + race + popdensity2010 + med_hhinc2016,
   data = long.data1a)

ncollege.seq <- seq(from = 0, to = 82, by = 1)
race.seq <- unique(long.data1a$race)
popdensity2010.seq <- seq(from = 0.106, to = 5635.804, by = 300)
med_hhinc2016.seq <- seq(from = 26645, to = 103043, by = 3750)
grid1 <- expand.grid('ncollege' = ncollege.seq,
                     'race' = race.seq,
                     'popdensity2010'  = popdensity2010.seq,
                     'med_hhinc2016' = med_hhinc2016.seq)
predictions <- predict.lm(long.lm1.race,
                          grid1)
final.data <- data.frame(grid1, predictions)


final.data %>%
  group_by(race, ncollege) %>% 
  summarise(meanpredict = mean(predictions)) %>%
  ggplot(aes(x = ncollege,
             y = meanpredict)) +
  geom_line(aes(color = factor(race)),
            size = 1.5) +
  theme_economist() +
  theme(plot.title = element_text(hjust = 0, size = 10, face = "bold")) +
  scale_color_brewer(palette = "YlOrRd") +
  ylab("Household Income Percentile Rank") +
  ggtitle("Effect of Number of Colleges in CZ on Household Income Percentile Rank by Race") + 
  labs(color = "")


```


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